
A set of user requests (or topics) \({Q}\)  in the conversational form (e.g. “What is Fickle Creek Farm?”) with a label reflects if clarification is needed ranged from 1 to 4;

A question bank which contains possible clarifying questions collected for the user queries via crowdsourcing [1]} (e.g. “Do you want to know the location of fickle creek farm?”); and

A set of user answers, one for each question (e.g. “No, I want to find out where can I purchase fickle creek farm products.”), which are generated via crowdsourcing. The answer \(A\)  to a clarification question \(Q^{\prime }\)  can be used to measure how much asking \(Q^{\prime }\)  can help improve the search result, thus providing supervision for training question selection models.

